






"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]					) ->							Optional[Any]:
  if not head:
    return True
  # split the list to two parts
  lowerCamelCase_	,      lowerCamelCase_       	=		head.next, head
  while fast and fast.next:
    lowerCamelCase_       	=		fast.next.next
    lowerCamelCase_       	=		slow.next
  lowerCamelCase_       	=		slow.next
  lowerCamelCase_       	=		None  # Don't forget here! But forget still works!
  # reverse the second part
  lowerCamelCase_       	=		None
  while second:
    lowerCamelCase_       	=		second.next
    lowerCamelCase_       	=		node
    lowerCamelCase_       	=		second
    lowerCamelCase_       	=		nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    lowerCamelCase_       	=		node.next
    lowerCamelCase_       	=		head.next
  return True







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]					) ->							Optional[Any]:
  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  lowerCamelCase_       	=		lowerCamelCase_       	=		lowerCamelCase_       	=		head
  while fast and fast.next:
    lowerCamelCase_	,      lowerCamelCase_       	=		fast.next.next, slow.next

  # 2. Push the second half into the stack
  lowerCamelCase_       	=		[slow.val]
  while slow.next:
    lowerCamelCase_       	=		slow.next
    stack.append(slow.val					)

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    lowerCamelCase_       	=		cur.next

  return True







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  if not head or not head.next:
    return True
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		0
  while head:
    if head.val in d:
      d[head.val].append(_lowerCamelCase					)
    else:
      lowerCamelCase_       	=		[pos]
    lowerCamelCase_       	=		head.next
    pos += 1
  lowerCamelCase_       	=		pos - 1
  lowerCamelCase_       	=		0
  for v in d.values():
    if len(_lowerCamelCase					) % 2 != 0:
      middle += 1
    else:
      lowerCamelCase_       	=		0
      for i in range(0						,							len(_lowerCamelCase					)					):
        if v[i] + v[len(_lowerCamelCase					) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True












"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




